Medical image processing apparatus and method to generate and display third parameters based on first and second images

ABSTRACT

According to an embodiment, a medical image processing apparatus includes a storage, processing circuitry and a display. The storage stores a first image obtained by capturing a target region of a subject and a second image obtained by capturing the target region. The circuitry is configured to modify a first parameter of each of pixels of the first image based on a second parameter of each corresponding pixel of the second image and a function of the second parameter to generate a third parameter. The display displays a display image based on the third parameter which the processing circuitry determines for each of the pixels.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromthe Japanese Patent Application No. 2015-013179, filed Jan. 27, 2015,the entire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a medical imageprocessing apparatus, a medical image processing method and a medicalimage diagnosis apparatus.

BACKGROUND

It is known in the art to form one image by the fusion of a plurality ofmedical images. For example, a technique for the fusion of a computedtomography (CT) image and a positron emission tomography (PET) image isknown in the art. According to this technique, an opacity curve and acolor map based on measurements are set for each pixel of the CT image.Similarly, the opacity curve and color map are set for each pixel of thePET image. After this processing, the two images are subjected tofusion.

By the image fusion processing, a medical doctor can obtain bothmorphologic information of the CT image and functional information ofthe PET image from one image. According to the image fusion processing,however, a region that does not have to be observed may also besubjected to fusion. For example, an affected region shown in the PETimage may overlap a bone region shown in the CT image. In such a fusionimage, the information may be congested, and a region of interest (ROI)may not be observed easily.

As described above, a fusion image obtained by the fusion of a pluralityof images according to the existing technique may not allow easyinterpretation of images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram showing an example of a PET/CTapparatus 10 according an embodiment;

FIG. 2 is a flowchart illustrating an example of a procedure performedby the medical image processing apparatus 100 shown in FIG. 1;

FIG. 3 is an explanatory diagram illustrating an example of processingfor correcting a base image (a CT image) by use of a sub image (a PETimage);

FIG. 4 is a diagram showing, for comparison, an example of a fusionimage prepared by the existing technique;

FIG. 5 is a diagram to explain functional images can be generated from aplurality of phase images; and

FIG. 6 is a diagram showing an example of a CT-perfusion (CTP) image.

DETAILED DESCRIPTION

In general, according to one embodiment, a medical image processingapparatus includes a storage, processing circuitry and a display. Thestorage stores a first image obtained by capturing a target region of asubject and a second image obtained by capturing the target region. Thecircuitry is configured to modify a first parameter of each of pixels ofthe first image based on a second parameter of each corresponding pixelof the second image and a function of the second parameter to generate athird parameter. The display displays a display image based on the thirdparameter which the processing circuitry determines for each of thepixels.

In the explanation set forth below, the medical image processingapparatus will be described as being incorporated in the PET/CTapparatus 10. However, the medical image processing apparatus may beincorporated in a modality other than the PET/CT apparatus 10, or may beused independently. In the description below, structural elements havingsubstantially the same functions and configurations will be denoted bythe same reference symbols, and a repetitive description will be givenonly where necessary.

FIG. 1 is a functional block diagram showing an example of a PET/CTapparatus 10 according an embodiment. The PET/CT apparatus 10 comprisesa medical image processing apparatus 100, a CT scanner 20, a CTprojection data collector 30, a morphologic image generator 40, a PETscanner 50, a PET projection data collector 60 and a functional imagegenerator 70.

The CT scanner 20 CT-scans a subject by X-rays. The CT scanner 20comprises an X-ray tube (not shown) and an X-ray detector (not shown).The X-ray tube and the X-ray detector are arranged to be opposite toeach other, with a hollow section (not shown) in which a subject liesbeing located in between. The X-ray tube generates X-rays. The X-raydetector detects X-rays that have passed through the subject andgenerates electric signals based on the energy of the detected X-rays.The X-ray generation and the X-ray detection are repeated, with theX-ray tube and the X-ray detector being rotated around the hollowsection.

The CT projection data collector 30 collects projection data(hereinafter referred to as CT projection data) by performingpreprocessing for the electric signals supplied from the CT scanner 20.The preprocessing performed for the generation of the CT projection dataincludes logarithmic conversion, sensitivity correction, beam hardeningcorrection, etc.

The morphologic image generator 40 generates morphologic images. Themorphologic image generator 40 generates CT image data regarding apredetermined reconstructed section, based on the CT projection data.The pixel value of each of the pixels of a CT image reflects a CT valuewhich is based on the X-ray attenuation coefficient (absorptioncoefficient) of a material existing on an X-ray transmission path. CTimages prepared by the morphologic image generator 40 are stored in thestorage 500 of the medical image processing apparatus 100.

The PET scanner 50 PET-scans a subject by gamma rays. The PET scannercomprises gamma ray detectors (not shown) annually arranged around thehollow section. The gamma ray detectors repeatedly detect gamma raysemitted from the subject and repeatedly generate electric signals basedon the energy of the detected gamma rays.

The PET projection data collector 60 performs signal processing for theelectric signals supplied from the PET scanner 50 and generatesprojection data (hereinafter referred to as PET projection data). Thesignal processing includes position calculation processing, energycalculation processing, coincidence counting processing andpreprocessing. The preprocessing used for the generation of PETprojection data includes, for example, sensitivity correction, randomcorrection, scattered radiation correction.

The functional image generator 70 generates functional images. Thefunctional image generator 70 generates PET image data regarding areconstructed section at substantially the same position as thereconstructed section of the CT image, based on the PET projection data.The pixel value of each of the pixels of a PET image reflects a countvalue which is based on the concentration of radioactive isotopes. PETimages prepared by the functional image generator 70 are stored in thestorage 500.

The medical image processing apparatus 100 comprises a controller 200, adisplay 300, an input section 400, a storage 500 and an interface 600.

The controller 200 includes, for example, a processor, a centralprocessing unit (CPU), a memory and so on.

The display 300 displays various kinds of information such as candidatethresholds. As the display 200, a CRT display, a liquid crystal display,an organic EL display, etc. can be used, as needed.

The input section 400 receives various commands or information which theoperator enters by operating an input device. As the input device, akeyboard, a mouse, various kinds of switches may be used.

The storage 500 is, for example, a hard disk drive (HDD), a solid stagedrive (SSD), or a storage device such as a semiconductor memory. Thestorage 500 stores a base image of an object to be displayed, and a subimage showing substantially the same portion as the base image.

In the embodiment, an image which is to be corrected is referred to as abase image. An image used for obtaining data required for the correctionprocessing is referred to as a sub image.

The interface 600 is connected to a picture archiving and communicationsystem (PACS) (not shown) or to another computer, by way of a network.The interface 600 communicates with a communication partner to which itis connected. The typical protocol used for this communication isdigital imaging and communications in medicine (DICOM).

The controller 200 comprises an image acquiring function 210, an imageselecting function 220, an image aligning function 230, a firstdetermining function 240, a second determining function 250, and a thirddetermining function 260.

The controller 200 functions as an information processing apparatus (acomputer) and controls the medical image processing apparatus 100. Thecontroller 200 reads various programs related to control processing fromthe storage 500. After reading program, the controller 200 has thefunctions shown in FIG. 1, namely, an image acquiring function 210, animage selecting function 220, an image aligning function 230, a firstdetermining function 240, a second determining function 250 and a thirddetermining function 260. In other words, the controller 200 reads, fromthe storage 500, programs related to the image acquiring function 210,the image selecting function 220, the image aligning function 230, thefirst determining function 240, the second determining function 250 andthe third determining function 260.

The controller 200 loads the program related to the image acquiringfunction in its memory, and executes the program to realize the imageacquiring function 210. At the time, the controller 200 functions as theimage acquiring function 210.

The image acquiring function 210 obtains CT images and PET images of anobject from the storage 500.

The controller 200 loads the program related to the image selectingfunction in its memory, and executes the program to realize the imageselecting function 220. At the time, the controller 200 functions as theimage selecting function 220.

The image selecting function 220 selects one base image and one or moresub images from among a plurality of images obtained from the storage500. It should be noted here that an image to be displayed is referredto as a base image. An image used for correcting the base image isreferred to as a sub image.

The image selecting function 220 stores the selected images in itsmemory (not shown). The images may be captured by different modalitiesor by one modality. The base images used in the present embodiment maybe morphological images or functional images. Similarly, the sub imagesmay be morphological images or functional images.

The morphological images are original images created by an X-ray CTapparatus, an X-ray photography apparatus, a magnetic resonance imaging(MRI) apparatus or the like. The morphological images containmorphological information. The morphological information is informationrepresenting the shapes of internal organs or bones.

The functional images are original images created by a PET apparatus, asingle photon emission computed tomography (SPECT) apparatus or thelike. The functional images are images representing functionalinformation and include information on motion parameters of an ROI, suchas a cardiac wall motion and an ejection fraction, or blood flow-relatedparameters of the ROI, such as a blood flow rate, a blood volume, a meantransit time and a wash-out rate. The functional images containfunctional information. The functional information is informationrepresenting the motion of an internal organ, a blood flow, metabolism,etc. A medical doctor can presume pathological abnormality such as atumor or cardiac infarction based on the functional information.

Let us assume that the base images of the present embodiment aremorphological images and the sub images are functional images. Morespecifically, let us assume that the base images of the presentembodiment are CT images and the sub images are PET images and that theCT images and the PET images are images of the same body portion of asubject. This is merely an example and is not restrictive.

The controller 200 loads the program related to the image aligningfunction in its memory, and executes the program to realize the imagealigning function 230. At the time, the controller 200 functions as theimage aligning function 230.

The image aligning function 230 performs positional adjustment betweenthe base images and sub images stored in the memory of the imageselecting function 220. An arbitrary algorithm may be used for thispositional adjustment.

The controller 200 loads the program related to the first determiningfunction in its memory, and executes the program to realize the firstdetermining function 240. At the time, the controller 200 functions asthe first determining function 240.

The first determining function 240 determines a first parameter relatedto how a base image should be displayed. The first parameter isdetermined based on the pixel value of each of the pixels of the baseimage. The parameter related to the display of an image is, for example,opacity or a color value. To simplify the description given below, it isassumed that the parameters of a base image and sub image are opacityand a color value. The first determining function 240 can adjust theparameters related to the image quality of the base image in such amanner that the ROI in the base image can be easily observed.

The controller 200 loads the program related to the second determiningfunction in its memory, and executes the program to realize the seconddetermining function 250. At the time, the controller 200 functions asthe second determining function 250.

The second determining function 250 determines a second parameterrelated to how a sub image should be displayed. The second parameter isdetermined based on the pixel value of each of the pixels of the subimage. The second determining function 250 can adjust the parametersrelated to the image quality of the sub image in such a manner that theROI in the sub image can be easily observed.

The controller 200 loads the program related to the third determiningfunction in its memory, and executes the program to realize the thirddetermining function 260. At the time, the controller 200 functions asthe third determining function 260.

The third determining function 260 determines a third parameter relatedto how a display image should be displayed. The third parameter isdetermined for each of the pixels of the base image. The third parameterreflects both the pixel values of the pixels of the base image and thepixel values of the pixels of the sub image. The third determiningfunction 260 determines the third parameter based on the first parameterrelated to the display of the base image and the second parameterrelated to the display of the sub image. A description will now be givenof an operation of the apparatus described above.

FIG. 2 is a flowchart illustrating an example of a procedure performedby the medical image processing apparatus 100 shown in FIG. 1. In theprocess mentioned below, it is assumed that the storage 500 stores aseries of CT images and a series of PET images in association with eachother. The medical image processing described below is performed for theCT image and the PET image which are included in the series of the CTimages and PET images and which have the same image capturing date, forexample.

Referring to FIG. 2, the controller 200 controls the image acquiringfunction 210 to acquire, from the storage 500, the CT images and PETimages which are captured in time series and are to be processed (StepS11). The CT images and the PET images may be acquired in response to acommand which the operator enters from the input section 400.

Then, the controller 200 controls the image selecting function 220 insuch a manner as to select one base image (e.g., a CT image) and one subimage (e.g., a PET image) from the CT and PET images acquired in StepS11 (Step S12). Two or more sub images may be selected. The base imageand the sub image may be selected in response to a command which theoperator enters from the input section 400.

Then, the controller 200 controls the image aligning function 230 toperform positional adjustment between the base image and sub imagestored in the memory of the image selecting function 220 (Step S13). InStep S13, the image aligning function 230 performs positional alignment,for example, by associating the base image and the sub image with eachother, based on the pixels showing the same anatomical body portion.

Subsequently, the controller 200 controls the first determining function240 to determine a first parameter related to how the base image shouldbe displayed. The first parameter is determined based on the pixel valueof each of the pixels of the base image (Step S14). In Step S14, thefirst determining function 240 adjusts the display parameters (opacityand color value) of the base image (CT image), which are based on the CTvalues and the window level/window width, automatically or in responseto a manual operation by the operator.

It is assumed that in the image adjusted in image quality, the opacityat the coordinate portion having a CT value of p is defined asO_(CT)(p). The opacity O_(CT) is a value in the range of 0.0 to 1.0. Thecoordinate portion is completely transparent when the opacity O_(CT) is0 and is completely opaque when the opacity is 1.0.

It is assumed that in the image adjusted in image quality, the colorvalue at the coordinate portion having a CT value of p is defined asC_(CT)(p). The color value C_(CT) is represented in the RGB notation,and C_(CT)(p)=(R(C_(CT)) G(C_(CT)) B(C_(CT))). R(C_(CT)) (hereinafterreferred to as the R element), G(C_(CT)) (hereinafter referred to as theG element) and the B(C_(CT)) (hereinafter referred to as the B element)are values in the range of 0 to 255. The first parameter regarding howthe base image should be displayed is determined as above.

Subsequently, the controller 200 controls the second determiningfunction 250 to determine a second parameter related to how the subimage should be displayed. The second parameter is determined based onthe pixel value of each of the pixels of the sub image (Step S15). InStep S15, the second determining function 250 adjusts the displayparameters (opacity and color value) of the sub image (PET image), whichare based on the measurements or SUV value and the window level/windowwidth, automatically or in response to a manual operation by theoperator.

The PET image has an index referred to as a standardized uptake value(SUV). The SUV can be correlated with the CT value of the CT image. TheSUV is an index representing how a radioactive agent is concentrated inan affected portion and is calculated based on the measured PET value.

The opacity at the coordinate portion where the SUV is q is defined asO_(PET)(q). The color value at the coordinate portion where the SUV is qis defined as C_(PET)(q). The second parameter regarding how the subimage should be displayed is determined as above.

Subsequently, the controller 200 controls the third determining function260 to determine a third parameter related to how the display imageshould be displayed. The third parameter is determined for each of thepixels of the base image (Step S16). The third parameter reflects boththe pixel values of the pixels of the base image and the pixel values ofthe pixels of the sub image. The third determining function 260determines the third parameter based on the first parameter related tothe display of the base image and the second parameter related to thedisplay of the sub image.

The third determining function 260 determines the third parametersrelated to the base image in such a manner that the second parameter andthe third parameter have a positive correlation. The positivecorrelation is intended to indicate that there is a similar tendencybetween the two parameters. That is, if one of them increases, the otheralso increases. Conversely, if one of them decreases, the other alsodecreases.

The third determining function 260 determines a third parameter relatedto how the base image (CT image) should be displayed. The thirdparameter is determined based on the SUV of the sub image (PET image).

A description will be given as to how the opacity of the third parameteris determined. The opacity O′_(CT) of the CT image subjected tocorrection processing is denoted by formula 1 set forth below.

$\begin{matrix}{O_{CT}^{\prime} = \{ \begin{matrix}f & ( {0 \leq f \leq 1} ) \\1 & ( {f > 1} ) \\0 & ( {f < 0} )\end{matrix} } & \lbrack {{formula}\mspace{14mu} 1} \rbrack\end{matrix}$where f is a correction function. The correction function f is definedby the CT value p of the CT image, the SUV q of the PET image, and thecontribution ratio α of the PET image to the CT image with respect tothe opacity. In the present embodiment, the correction function f isdefined by either formula (1) or formula (2) below.f(p,q,α)=O _(CT)(p)−α(1−O _(PET)(q))  (1)f(p,q,α)=O _(CT)(p)·exp{−α(1−O _(PET)(q))}  (2)where f is defined in such a manner as to decrease in accordance with adecrease of the value of O_(PET). The contribution ratio α representshow the the opacity O_(PET) of the sub image contributes the opacity inthe corrected display image. When the value of α is large, the degree ofcorrection is high. When the value of α is zero, correction is notperformed. The maximal value of α is 1. The contribution ratio α can befreely designated as a display parameter by a medical doctor whooperates the input section 400.

By displaying O′^(CT) as an image using the correction function fdefined by formula (1) or formula (2), the transparent regions of thePET image (i.e., the regions where the opacity is low) are displayed astransparent in the corrected image as well. In other words, the use ofthe correction function f enables the opacity O′_(CT) of the displayimage to have a tendency similar to that of O_(PET).

A description will be given as to how the color value (including the Relement, G element and B element) of the third parameter is determined.The color value of the base image (CT image) is corrected based on theSUV of the sub image (PET image). When the color value of the CT imagesubjected to correction processing is C′_(CT), the R element of C′_(CT)is denoted by formula 2 set forth below.

$\begin{matrix}{{R( C_{CT}^{\prime} )} = \{ \begin{matrix}g & ( {0 \leq g \leq 255} ) \\255 & ( {g > 255} ) \\0 & ( {g < 0} )\end{matrix} } & \lbrack {{formula}\mspace{14mu} 2} \rbrack\end{matrix}$where g is a correction function. The correction function g is definedby the CT value p of the CT image, the SUV q of the PET image, and thecontribution ratio β of the PET image to the CT image with respect tothe color value. In the present embodiment, the correction function g isdefined by either formula (3) or formula (4) below.g(p,q,β)=R(C _(CT)(p))−β(255−R(C _(PET)(q)))  (3)g(p,q,β)=R(C _(CT)(p))·exp{−β(255−R(C _(PET)(q)))}  (4)where g is defined in such a manner as to decrease in accordance with adecrease of the value of C_(PET). The contribution ratio β representshow the the R element of C_(PET) of the sub image contributes the Relement in the corrected display image. When the value of β is large,the degree of correction is high. When the value of β is zero,correction is not performed. The maximal value of β is 1. Thecontribution ratio β can be freely designated as a display parameter bya medical doctor who operates the input section 400.

By displaying C′_(CT) as an image using the correction function gdefined by formula (3) or formula (4), the small-R-element regions ofthe PET image are displayed as small-R-element regions in the correctedimage as well. In other words, the use of the correction function genables the R element of the color value C′_(CT) of the display image tohave a tendency similar to that of C_(PET). Similar processing isexecuted for the G element and B element of C′_(CT).

The controller 200 controls the display 300 to display a base imagebased on the third parameter (Step S17). In Step S17, the display 300displays a base image corrected based on the third parameter, namely, adisplay image. The image displayed on the display 300 mainly uses a CTimage having morphological information and yet contains functionalinformation, namely, the SUV distribution of a PET image.

FIG. 3 is an explanatory diagram illustrating an example of processingfor correcting a base image (a CT image) by use of a sub image (a. PETimage). The display image mainly shows morphological information of theCT image and yet shows high-SUV regions (i.e., affected portions) of thePET image in an emphasized manner. That is, the display image shown inFIG. 3 reflects both the morphological information and the functionalinformation.

FIG. 4 is a diagram showing, for comparison, an example of a fusionimage prepared by the existing technique. According to the existingfusion processing, an opacity distribution (opacity curve) and a colordistribution (color map) based on measurements are set for each pixel ofa CT image. Similarly, the opacity curve and color map are set for eachpixel of a PET image. The two images are superimposed to generate afusion image.

The existing fusion processing, however, does not involve the correctionprocessing described above. For this reason, the fusion image inevitablyincludes regions that are not required in the observation. For example,in FIG. 4, the affected regions of the PET image are superimposed on thebone regions of the CT image and are hard to observe due to thecongestion of information. That is, according to the existing technique,superimposition is performed for each of the pixels of an image,regardless of whether the pixels are those of a region to be observed.As a result, information is congested in the display image, and theregions to be observed are hard to discriminate from the regions that donot have to be observed. This prevents a medical doctor from accuratelyinterpreting the images in the regions to be observed.

It is known in the art to use the measurements of one of two images forthe processing of the other image. For example, an inhibition regionthat inhibits clear observation of regions to be observed can beidentified based on measurements of one of the images, and an imagewithout the inhibition region can be superimposed on the other image.This technique may enable clear observation of the regions to beobserved because of the deletion of the inhibition region.

However, this technique removes every image information regarding thedeleted region. For this reason, information representing how themeasurements vary with time cannot be obtained. In addition, sincesimple fusion processing is performed in regions other than theinhibition region, the interpretation of an image may be hard due to thecongestion of information.

In contrast, the medical image processing apparatus of the presentembodiment can correct a base image based on a sub image. In a displayimage obtained by the correction, the display parameters of the baseimage (such as the opacity and color value of a CT image) have atendency similar to those of the sub image (PET image). Furthermore,unlike the fusion image shown in FIG. 4, the fusion image according tothe embodiment enables the observation regions to be discriminated fromthe other regions.

Hence, the embodiment can provide a medical image processing apparatus,a medical image processing method and a medical image diagnosisapparatus capable of creating fusion images that are easy to interpret.

The embodiment described above is not restrictive. In theabove-mentioned embodiment, a CT image is used as a base image, and aPET image is used as a sub image. Instead of this, the base image may bea morphological image or a functional image. Similarly, the sub imagemay be a morphological image or a functional image. In addition, adisplay image may be superimposed on another image (e.g., a sub image)after being adjusted in position.

Functional images can be generated from a plurality of images ofdifferent time phases.

FIG. 5 is a diagram to explain functional images can be generated from aplurality of phase images. In the field of the medical image processingtechnology, analyzing a plurality of time phase images (for example, CTimages) allows to generate a functional image. Namely, it is possible togenerate a functional image from a plurality of time phase images. Inthis case, because the functional image is generated after completion ofpositional adjustment among the plurality of time phase images, thepositional alignment process of Step S13 of the flowchart shown in FIG.2 may be omitted. Especially, in such case that the functional image andthe morphological image are generated by using the same time phase asstandard, the positional alignment process of Step S13 may be omitted.

In the case shown in FIG. 5, an example of generating a functional imageon the basis of the CT images is explained. Instead of this, functionalimages can be generated from a plurality of time phase images by usingother kind of medical images (for example, MR images or ultrasonicimages and so on).

FIG. 6 is a diagram showing an example of a CT-perfusion (CTP) image ofa brain. In CTP, CT images for a plurality of time phases are generatedby the continuous scan imaging performed while the first circulation ofthe travenous injected iodinated contrast medium in the brain. Becauseof the iodinated contrast medium cannot flow through the blood-brainbarrier, a perfusion evaluation can be performed in the brain tissuecapillary bed level. The CTP images generated by the above method can becalled as an example of functional image generated with a plurality oftime phase images.

FIG. 6(a) shows CTP images generated with continuous scanning. FIG. 6(b)and FIG. 6(c) show CTP images generated with intermittent scanning.Cerebral blood flow (CBF) images, perfusion maps of mean transit time(MTT) and perfusion maps of time to peak (TTP) are shown in FIG. 6.

In the embodiment, it is explained that the third determining function260 determines the third parameters related to the base image in such amanner that the second parameter and the third parameter have a positivecorrelation. Alternately, the third determining function 260 maydetermine the third parameters related to the base image in such amanner that the second parameter and the third parameter have a negativecorrelation. Further, whether the positive correlation is applied or thenegative correlation is applied may be switched on the basis of the kindof the second parameter (physical values shown in the pixels of thefunctional image). The negative correlation is intended to indicate thatthere is a opposite tendency between the two parameters. That is, if oneof them increases, the other decreases. Conversely, if one of themdecreases, the other increases.

The modality is not limited to a PET/CT apparatus. Images obtained by anX-ray photography apparatus, an MRI system, a SPECT apparatus or othermodalities can be processed in a similar manner. The kinds of images arenot limited to a CT image or a PET image. A plain X-ray image, an MRIimage, an ultrasonic diagnosis image, an angiographic image, an infraredtopography apparatus image and a SPECT image may be used.

The term “processor” used in the above descriptions is, for example, acentral processing unit (CPU) or a graphics processing unit (GPU), ormay include the following types of circuit: an application-specificintegrated circuit (ASIC), a programmable logic device (such as a simpleprogrammable logic device (SPLD), or a complex programmable logic device(CPLD), a field programmable gate array (EPGA) or the like. Theprocessor reads the programs stored in the storage circuit and executesthem to realize the respective functions. The programs may beincorporated in the circuit of the processor, instead of storing them inthe storage circuit. In this case, the processor reads the programsincorporated in its circuit and executes them to realize the respectivefunctions. The processors described in connection with the aboveembodiments are not limited to single-circuit processors. A plurality ofindependent processors may be combined and integrated as one processorhaving multiple functions. Furthermore, a plurality of structuralelements of each of the above embodiments may be integrated as oneprocessor having multiple functions.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope. Indeed, the novel embodiments described herein may be embodied ina variety of other forms; furthermore, various omissions, substitutionsand changes in the form of the embodiments described herein may be madewithout departing from the spirit. The accompanying claims and theirequivalents are intended to cover such forms or modifications as wouldfall within the scope and spirit.

The invention claimed is:
 1. A medical image processing apparatus,comprising: a memory which stores a first image, which is a CT imageobtained by capturing a target region of a subject, and a second image,which is a PET image obtained by capturing the target region; processingcircuitry configured to modify a first parameter of each pixel of thefirst image based on a second parameter of each corresponding pixel ofthe second image and based on a function of the second parameter, togenerate a third parameter corresponding to each pixel, wherein thefunction includes a contribution ratio of the second image to the firstimage; and a display which displays a display image based on thegenerated third parameter for each of the pixels, wherein when the firstparameter, the second parameter, and the third parameter representopacities, the third parameter O′_(CT) is defined as$O_{CT}^{\prime} = \{ \begin{matrix}f & ( {0 \leq f \leq 1} ) \\1 & ( {f > 1} ) \\0 & ( {f < 0} )\end{matrix} $ where f is defined asf(p,q,α)=O_(CT)(p)−α(1−O_(PET)(q)) or f(p,q,α)=O_(CT)(p)·exp{−α(1−O_(PET)(q))}, and O_(CT)(p) is the first parameter of the firstimage, and O_(PET)(q) is the second parameter of the second image, and ais a contribution ratio; and when the first parameter, the secondparameter, and the third parameter represent color values, the thirdparameter E(C′_(CT)) is defined as${E( C_{CT}^{\prime} )} = \{ \begin{matrix}e & ( {0 \leq e \leq 255} ) \\255 & ( {e > 255} ) \\0 & ( {e < 0} )\end{matrix} $ where e represents R element, G element, and Belement, and is defined as e(p,q,β)=E(C_(CT)(p))−β(255−E(C_(PET)(q))) ore(p,q,β)=E(C_(CT)(p))·exp {−β(255−(E_(PET)(q)))}, and C_(CT)(p) is thefirst parameter of the first image, C_(PET)(q) is the second parameterof the second image, β is a contribution ratio.
 2. The medical imageprocessing apparatus according to claim 1, wherein: the first image is amorphological image, and the second image is a functional image.
 3. Themedical image processing apparatus according to claim 1, wherein: thefirst parameter and the second parameter comprise at least one of pixelvalues including opacity and a color value.
 4. The medical imageprocessing apparatus according to claim 1, wherein: the processingcircuitry determines, for each pixel, the third parameter such that thethird parameter and the second parameter have a positive correlation ora negative correlation.
 5. The medical image processing apparatusaccording to claim 4, wherein: the processing circuitry switches thepositive correlation or the negative correlation based on a kind ofphysical values shown in the pixels of the second image.
 6. The medicalimage processing apparatus according to claim 1, wherein: the secondimage is a functional image generated from a plurality of time phaseimages.
 7. The medical image processing apparatus according to claim 1,wherein the function includes the contribution ratio of the second imageto the first image with respect to an opacity.
 8. The medical imageprocessing apparatus according to claim 1, wherein the function includesthe contribution ratio of the second image to the first image withrespect to a color value.
 9. The medical image processing apparatus ofclaim 1, wherein the processing circuitry is further configured toreceive a value of the contribution ratio, which is determined based oninput by a user.
 10. The medical image processing apparatus of claim 1,wherein each pixel of the display image is equal to a CT value of acorresponding pixel in the first image.
 11. A medical image processingapparatus, comprising: a memory which stores a first image, which is aCT image obtained by capturing a target region of a subject and a secondimage, which is a PET image obtained by capturing the target region;processing circuitry configured to modify a first parameter of eachpixel of the first image based on a second parameter of eachcorresponding pixel of the second image and based on a function of thesecond parameter, to generate a third parameter corresponding to eachpixel, wherein the function includes a contribution ratio of the secondimage to the first image; and a display which displays a display imagein which a region corresponding to the subject of the first image isshown, by assigning the third parameter to each of the pixels generatedby the processing circuitry, wherein when the first parameter, thesecond parameter, and the third parameter represent opacities, the thirdparameter O′_(CT) is defined as$O_{CT}^{\prime} = \{ \begin{matrix}f & ( {0 \leq f \leq 1} ) \\1 & ( {f > 1} ) \\0 & ( {f < 0} )\end{matrix} $ where f is defined asf(p,q,α)=O_(CT)(p)−α(1−O_(PET)(q)) or f(p,q,α)=O_(CT)(P)_exp{−α(1−O_(PET)(q))}, and O_(CT)(p) is the first parameter of the firstimage, and O_(PET)(q) is the second parameter of the second image, and ais a contribution ratio; and when the first parameter, the secondparameter, and the third parameter represent color values, the thirdparameter E(C′_(CT)) is defined as${E( C_{CT}^{\prime} )} = \{ \begin{matrix}e & ( {0 \leq e \leq 255} ) \\255 & ( {e > 255} ) \\0 & ( {e < 0} )\end{matrix} $ where e represents R element, G element, and Belement, and is defined as e(p,q,β)=E(C_(CT)(p))−β(255−E(C_(PET)(q))) ore(p,q,β)=E(C_(CT)(p))·exp {−β(255−(E_(PET)(q)))}, and C_(CT)(p) is thefirst parameter of the first image, C_(PET)(q) is the second parameterof the second image, β is a contribution ratio.
 12. A medical imageprocessing method, comprising: modifying a first parameter of each pixelof a first image, which is a CT image obtained by capturing a targetregion of a subject, based on a second parameter of each correspondingpixel of a second image, which is a PET image obtained by capturing thetarget region and based on a function of the second parameter, therebygenerating a third parameter corresponding to each pixel, wherein thefunction includes a contribution ratio of the second image to the firstimage; and displaying a display image based on the generated thirdparameter determined for each of the pixels, wherein when the firstparameter, the second parameter, and the third parameter representopacities, the third parameter O′_(CT) is defined as$O_{CT}^{\prime} = \{ \begin{matrix}f & ( {0 \leq f \leq 1} ) \\1 & ( {f > 1} ) \\0 & ( {f < 0} )\end{matrix} $ where f is defined asf(p,q,α)=O_(CT)(p)−α(1−O_(PET)(q)) or f(p,q,α)=O_(CT)(p)·exp{−α(1−O_(PET)(q))}, and O_(CT)(p) is the first parameter of the firstimage, and O_(PET)(q) is the second parameter of the second image, and ais a contribution ratio; and when the first parameter, the secondparameter, and the third parameter represent color values, the thirdparameter E(C′_(CT)) is defined as${E( C_{CT}^{\prime} )} = \{ \begin{matrix}e & ( {0 \leq e \leq 255} ) \\255 & ( {e > 255} ) \\0 & ( {e < 0} )\end{matrix} $ where e represents R element, G element, and Belement, and is defined as e(p,q,β)=E(C_(CT)(p)−β(255−E(C_(PET)(q))) ore(p,q,β)=E(C_(CT)(p))·exp {−β(255−(E_(PET)(q)))}, and C_(CT)(p) is thefirst parameter of the first image, C_(PET)(q) is the second parameterof the second image, β is a contribution ratio.
 13. A medical imagediagnosis apparatus, comprising: first processing circuitry configuredto capture a first image, which is a CT image of a target region of asubject, and a second image, which is a PET image of the target regionof the subject; second processing circuitry configured to modify a firstparameter of each pixel of the first image based on a second parameterof each corresponding pixel of the second image and based on a functionof the second parameter, to generate a third parameter corresponding toeach pixel, wherein the function includes a contribution ratio of thesecond image to the first image; and a display which displays a displayimage based on the generated third parameter for each of the pixels,wherein when the first parameter, the second parameter, and the thirdparameter represent opacities, the third parameter O′_(CT) is defined as$O_{CT}^{\prime} = \{ \begin{matrix}f & ( {0 \leq f \leq 1} ) \\1 & ( {f > 1} ) \\0 & ( {f < 0} )\end{matrix} $ where f is defined asf(p,q,α)=O_(CT)(p)−α(1−O_(PET)(q)) or f(p,q,α)=O_(CT)(p)·exp{−α(1−O_(PET)(q))}, and O_(CT)(p) is the first parameter of the firstimage, and O_(PET)(q) is the second parameter of the second image, and ais a contribution ratio; and when the first parameter, the secondparameter, and the third parameter represent color values, the thirdparameter E(C′_(CT)) is defined as${E( C_{CT}^{\prime} )} = \{ \begin{matrix}e & ( {0 \leq e \leq 255} ) \\255 & ( {e > 255} ) \\0 & ( {e < 0} )\end{matrix} $ where e represents R element, G element, and Belement, and is defined as e(p,q,β)=E(C_(CT)(p))−β(255−E(C_(PET)(q))) ore(p,q,β)=E(C_(CT)(p))·exp {−β(255−(E_(PET)(q)))}, and C_(CT)(p) is thefirst parameter of the first image, C_(PET)(q) is the second parameterof the second image, β is a contribution ratio.